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from typing import Tuple, Union
from diffusers.models.autoencoders.autoencoder_dc import EfficientViTBlock
from diffusers.models.normalization import RMSNorm, get_normalization
from diffusers.models.activations import get_activation
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
norm_type: str = "batch_norm",
act_fn: str = "relu6",
rank_ratio: int = 16,
) -> None:
super().__init__()
self.norm_type = norm_type
self.nonlinearity = get_activation(act_fn) if act_fn is not None else nn.Identity()
if rank_ratio is None:
self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False)
else:
assert isinstance(rank_ratio, int)
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, in_channels // rank_ratio, 3, 1, 1, bias=False),
nn.Conv2d(in_channels // rank_ratio, in_channels, 3, 1, 1),
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels, out_channels // rank_ratio, 3, 1, 1, bias=False),
nn.Conv2d(out_channels // rank_ratio, out_channels, 3, 1, 1, bias=False),
)
self.norm = get_normalization(norm_type, out_channels)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
residual = hidden_states
hidden_states = self.conv1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.norm_type == "rms_norm":
# move channel to the last dimension so we apply RMSnorm across channel dimension
hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1)
else:
hidden_states = self.norm(hidden_states)
return hidden_states + residual
def get_block(
block_type: str,
in_channels: int,
out_channels: int,
attention_head_dim: int,
norm_type: str,
act_fn: str,
qkv_mutliscales: Tuple[int] = (),
):
if block_type == "ResBlock":
block = ResBlock(in_channels, out_channels, norm_type, act_fn)
elif block_type == "EfficientViTBlock":
block = EfficientViTBlock(
in_channels, attention_head_dim=attention_head_dim, norm_type=norm_type, qkv_multiscales=qkv_mutliscales
)
else:
raise ValueError(f"Block with {block_type=} is not supported.")
return block
class DCDownBlock2d(nn.Module):
def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None:
super().__init__()
self.downsample = downsample
self.factor = 2
self.stride = 1 if downsample else 2
self.group_size = in_channels * self.factor**2 // out_channels
self.shortcut = shortcut
out_ratio = self.factor**2
if downsample:
assert out_channels % out_ratio == 0
out_channels = out_channels // out_ratio
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=self.stride,
padding=1,
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
x = self.conv(hidden_states)
if self.downsample:
x = F.pixel_unshuffle(x, self.factor)
if self.shortcut:
y = F.pixel_unshuffle(hidden_states, self.factor)
y = y.unflatten(1, (-1, self.group_size))
y = y.mean(dim=2)
hidden_states = x + y
else:
hidden_states = x
return hidden_states
class DCUpBlock2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
interpolate: bool = False,
shortcut: bool = True,
interpolation_mode: str = "nearest",
) -> None:
super().__init__()
self.interpolate = interpolate
self.interpolation_mode = interpolation_mode
self.shortcut = shortcut
self.factor = 2
self.repeats = out_channels * self.factor**2 // in_channels
out_ratio = self.factor**2
if not interpolate:
out_channels = out_channels * out_ratio
self.conv = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if self.interpolate:
x = F.interpolate(hidden_states, scale_factor=self.factor, mode=self.interpolation_mode)
x = self.conv(x)
else:
x = self.conv(hidden_states)
x = F.pixel_shuffle(x, self.factor)
if self.shortcut:
y = hidden_states.repeat_interleave(self.repeats, dim=1, output_size=hidden_states.shape[1] * self.repeats)
y = F.pixel_shuffle(y, self.factor)
hidden_states = x + y
else:
hidden_states = x
return hidden_states
class DCAEEncoder(nn.Module):
def __init__(
self,
in_channels: int,
latent_channels: int,
attention_head_dim: int = 32,
block_type: Union[str, Tuple[str]] = "ResBlock",
block_out_channels: Tuple[int] = (128, 256, 512, 512, 1024, 1024),
layers_per_block: Tuple[int] = (2, 2, 2, 2, 2, 2),
qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (), (5,), (5,), (5,)),
downsample_block_type: str = "pixel_unshuffle",
out_shortcut: bool = True,
):
super().__init__()
num_blocks = len(block_out_channels)
if isinstance(block_type, str):
block_type = (block_type,) * num_blocks
if layers_per_block[0] > 0:
self.conv_in = nn.Conv2d(
in_channels,
block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1],
kernel_size=3,
stride=1,
padding=1,
)
else:
self.conv_in = DCDownBlock2d(
in_channels=in_channels,
out_channels=block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1],
downsample=downsample_block_type == "pixel_unshuffle",
shortcut=False,
)
down_blocks = []
for i, (out_channel, num_layers) in enumerate(zip(block_out_channels, layers_per_block)):
down_block_list = []
for _ in range(num_layers):
block = get_block(
block_type[i],
out_channel,
out_channel,
attention_head_dim=attention_head_dim,
norm_type="rms_norm",
act_fn="silu",
qkv_mutliscales=qkv_multiscales[i],
)
down_block_list.append(block)
if i < num_blocks - 1 and num_layers > 0:
downsample_block = DCDownBlock2d(
in_channels=out_channel,
out_channels=block_out_channels[i + 1],
downsample=downsample_block_type == "pixel_unshuffle",
shortcut=True,
)
down_block_list.append(downsample_block)
down_blocks.append(nn.Sequential(*down_block_list))
self.down_blocks = nn.ModuleList(down_blocks)
self.conv_out = nn.Conv2d(block_out_channels[-1], latent_channels, 3, 1, 1)
self.out_shortcut = out_shortcut
if out_shortcut:
self.out_shortcut_average_group_size = block_out_channels[-1] // latent_channels
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.conv_in(hidden_states)
for down_block in self.down_blocks:
hidden_states = down_block(hidden_states)
if self.out_shortcut:
x = hidden_states.unflatten(1, (-1, self.out_shortcut_average_group_size))
x = x.mean(dim=2)
hidden_states = self.conv_out(hidden_states) + x
else:
hidden_states = self.conv_out(hidden_states)
return hidden_states
class DCAEDecoder(nn.Module):
def __init__(
self,
TYPE: str,
in_channels: int,
latent_channels: int,
attention_head_dim: int = 32,
block_type: Union[str, Tuple[str]] = "ResBlock",
block_out_channels: Tuple[int] = (128, 256, 512, 512, 1024, 1024),
layers_per_block: Tuple[int] = (2, 2, 2, 2, 2, 2),
qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (), (5,), (5,), (5,)),
norm_type: Union[str, Tuple[str]] = "rms_norm",
act_fn: Union[str, Tuple[str]] = "silu",
upsample_block_type: str = "pixel_shuffle",
in_shortcut: bool = True,
):
super().__init__()
num_blocks = len(block_out_channels)
if isinstance(block_type, str):
block_type = (block_type,) * num_blocks
if isinstance(norm_type, str):
norm_type = (norm_type,) * num_blocks
if isinstance(act_fn, str):
act_fn = (act_fn,) * num_blocks
self.conv_in = nn.Conv2d(latent_channels, block_out_channels[-1], 3, 1, 1)
self.in_shortcut = in_shortcut
if in_shortcut:
self.in_shortcut_repeats = block_out_channels[-1] // latent_channels
up_blocks = []
for i, (out_channel, num_layers) in reversed(list(enumerate(zip(block_out_channels, layers_per_block)))):
up_block_list = []
if i < num_blocks - 1 and num_layers > 0:
upsample_block = DCUpBlock2d(
block_out_channels[i + 1],
out_channel,
interpolate=upsample_block_type == "interpolate",
shortcut=True,
)
up_block_list.append(upsample_block)
for _ in range(num_layers):
block = get_block(
block_type[i],
out_channel,
out_channel,
attention_head_dim=attention_head_dim,
norm_type=norm_type[i],
act_fn=act_fn[i],
qkv_mutliscales=qkv_multiscales[i],
)
up_block_list.append(block)
up_blocks.insert(0, nn.Sequential(*up_block_list))
self.up_blocks = nn.ModuleList(up_blocks)
channels = block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1]
self.norm_out = RMSNorm(channels, 1e-5, elementwise_affine=True, bias=True)
self.conv_act = nn.ReLU()
self.conv_out = None
if layers_per_block[0] > 0:
self.conv_out = nn.Conv2d(channels, in_channels, 3, 1, 1)
else:
self.conv_out = DCUpBlock2d(
channels, in_channels, interpolate=upsample_block_type == "interpolate", shortcut=False
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if self.in_shortcut:
x = hidden_states.repeat_interleave(
self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats
)
hidden_states = self.conv_in(hidden_states) + x
else:
hidden_states = self.conv_in(hidden_states)
for up_block in reversed(self.up_blocks):
hidden_states = up_block(hidden_states)
hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1)
hidden_states = self.conv_act(hidden_states)
hidden_states = self.conv_out(hidden_states)
return hidden_states
if __name__ == "__main__":
device = torch.device("cuda:0")
# case 1: from 32 x 8 x 8 to 3 x 256 x 256
data = torch.randn(1, 32, 8, 8).to(device)
model = DCAEDecoder(
in_channels=3,
latent_channels=32,
attention_head_dim=32,
block_type=["ResBlock", "ResBlock", "ResBlock",
"EfficientViTBlock", "EfficientViTBlock", "EfficientViTBlock"],
block_out_channels=[128, 256, 512, 512, 1024, 1024],
layers_per_block=[3, 3, 3, 3, 3, 3],
qkv_multiscales=[[], [], [], [5], [5], [5]],
norm_type="rms_norm",
act_fn="silu",
upsample_block_type="interpolate",
).to(device)
output = model(data)
num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Number of parameters: {num_params / 1e6:.2f}M")
print(f"Output shape: {output.shape}")
# case 2: from 2 x 2 x 2 to 32 x 8 x 8
data = torch.randn(1, 2, 2, 2).to(device)
model = DCAEDecoder(
in_channels=32,
latent_channels=2,
attention_head_dim=32,
block_type=["EfficientViTBlock", "EfficientViTBlock", "EfficientViTBlock"],
block_out_channels=[256, 512, 512],
layers_per_block=[6, 6, 6],
qkv_multiscales=[[5], [5], [5]],
norm_type="rms_norm",
act_fn="silu",
upsample_block_type="interpolate",
).to(device)
output = model(data)
num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Number of parameters: {num_params / 1e6:.2f}M")
print(f"Output shape: {output.shape}") |